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Duration 14 hours
Course Outline
Foundations of Multimodal LLMs in Vertex AI
- Exploration of multimodal features in Vertex AI
- Gemini models and their supported data modalities
- Applications in enterprise and research sectors
Configuring the Development Environment
- Setup of Vertex AI for multimodal processing
- Management of datasets spanning different modalities
- Practical session: environment configuration and data preparation
Long Context Windows for Advanced Reasoning
- Concepts behind long-context workflow architectures
- Applications in strategic planning and decision-making
- Practical session: executing long-context analysis tasks
Designing Cross-Modal Workflows
- Synthesizing text, audio, and image processing
- Sequencing multimodal stages within pipelines
- Practical session: structuring a multimodal pipeline
Managing Gemini API Parameters
- Configuration of multimodal inputs and result outputs
- Enhancing inference speed and resource efficiency
- Practical session: adjusting Gemini API settings for optimal performance
Sophisticated Applications and Integrations
- Creation of interactive multimodal agents and assistants
- Incorporation of external APIs and third-party tools
- Practical session: development of a complete multimodal application
Assessment and Continuous Improvement
- Testing strategies for multimodal system performance
- Evaluation metrics for accuracy, alignment, and model drift
- Practical session: assessing the efficacy of multimodal workflows
Conclusion and Path Forward
Requirements
- Strong proficiency in Python programming
- Hands-on experience with machine learning model development
- Working knowledge of multimodal data types (text, audio, image)
Intended Audience
- AI researchers
- Advanced developers
- Machine learning scientists
